---
name: mlops-and-platform
description: "MLOps and Platform: 23 concepts, no maths assumed."
version: 1.0.0
author: Learn AI
license: CC-BY-4.0
metadata:
  hermes:
    tags: [Mlops, And, Platform]
    related_skills: [ai-agents, data-engineering-and-analytics, llms-and-generative-ai, mathematical-foundations]
    source: https://basavapurushottam.com/learn-ai
---
# MLOps and Platform

23 concepts, written for an adult with no computer science and no mathematics past school arithmetic. Each concept is a file under `references/`; load one when it is the thing being asked about rather than reading the whole domain.


## When to Use

- The reader asks about mlops and platform and wants it explained from the ground up rather than summarised.

- A term below comes up and you want the plain-language account of it, with a worked example.

- You need to know what a concept rests on before teaching it — every reference lists its prerequisites.


## When NOT to Use

- The reader wants current events, papers or library documentation. This is a curriculum, not a news source, and it does not update itself.

- The reader wants code they can ship. Examples here are written to be understood, not deployed.


## How to Run

Open the file listed beside a concept in Quick Reference below. The files live in the `references` directory, are plain Markdown with no front matter and no dependencies — read the one you want and ignore the rest.


## Quick Reference


### Cloud Computing

- `references/edge-and-fog-computing.md` — **Edge and Fog Computing**: Moving computation toward where data is produced — on devices or at intermediate nodes…

### MLOps

- `references/azure-ml-studio.md` — **Azure ML Studio**: A cloud workspace for building, training and deploying models with both visual and code…
- `references/canary-releases.md` — **Canary Releases**: Routing a small traffic share to a new version, comparing metrics, and expanding only i…
- `references/code-generation-for-deployment.md` — **Code Generation for Deployment**: Compiling models or analytical code into standalone artifacts — C, ONNX or similar — fo…
- `references/drift-detection.md` — **Drift Detection**: Monitoring for changes in input distribution, prediction distribution or the input-outp…
- `references/human-in-the-loop-feedback.md` — **Human-in-the-Loop Feedback**: Designed points where a person reviews, corrects or approves system output, feeding jud…
- `references/mlflow-tracing.md` — **MLflow Tracing**: Recording the full execution of a generative request — prompts, retrievals, tool calls,…
- `references/model-registry-and-versioning.md` — **Model Registry and Versioning**: A catalogue of trained models with versions, stages, aliases and lineage, making promot…
- `references/model-serving-apis.md` — **Model Serving APIs**: The HTTP or RPC surface exposing a model, including schema validation, health checks an…
- `references/production-feedback-loops.md` — **Production Feedback Loops**: Channelling real usage signals — corrections, ratings, downstream outcomes — back into…
- `references/release-and-rollback-safety.md` — **Release and Rollback Safety**: Making deployments reversible — versioned artifacts, staged rollout and a rehearsed pat…
- `references/tall-array-computation.md` — **Tall Array Computation**: Applying familiar array operations to data larger than memory, with execution deferred…
- `references/trace-first-development.md` — **Trace-First Development**: Instrumenting before optimising, so every iteration is guided by observed behaviour rat…
- `references/training-serving-skew.md` — **Training-Serving Skew**: Divergence between the transformations applied during training and those applied in pro…

### Self-Hosting

- `references/backup-and-hardware-failure.md` — **Backup and Hardware Failure**: Planning for disks and machines failing — backup strategy, offsite copies and tested re…
- `references/container-orchestration-basics.md` — **Container Orchestration Basics**: Managing container lifecycle across restarts and hosts — scheduling, health checks, res…
- `references/data-ownership-and-privacy.md` — **Data Ownership and Privacy**: Keeping data under your own control rather than a third party's, the primary motivation…
- `references/docker-compose.md` — **Docker Compose**: Declaring a multi-container application in one file — services, networks, volumes and d…
- `references/lts-distribution-choice.md` — **LTS Distribution Choice**: Choosing a long-term-support Linux release so security updates continue for years witho…
- `references/portainer-management.md` — **Portainer Management**: A web interface for managing Docker environments — containers, images, volumes, network…
- `references/reverse-proxy-and-tls.md` — **Reverse Proxy and TLS**: Fronting internal services with a proxy that terminates TLS, routes by hostname and iss…
- `references/tailscale-mesh-networking.md` — **Tailscale Mesh Networking**: A peer-to-peer VPN creating a private network across devices without exposing services…
- `references/wsl-and-vm-setup.md` — **WSL and VM Setup**: Running Linux inside Windows or macOS through a subsystem or virtual machine, to get a…


## Pitfalls

- The concepts are ordered by what rests on what, not by difficulty. A later concept is not always harder; it is later because something else has to come first.

- Explanations avoid notation the reader has not met. If you reintroduce standard symbols, say what they mean, or you undo the thing this curriculum is for.


## Verification

`ls references/ | wc -l` should print 23.


---

From [Learn AI](https://basavapurushottam.com/learn-ai). Prose is licensed CC BY 4.0.
